The peridynamic theory of solid mechanics is applied to the continuum modeling of the impact of small, high-velocity silica spheres on multilayer graphene targets. The model treats the laminate as a brittle elastic membrane. The material model includes separate failure criteria for the initial rupture of the membrane and for propagating cracks. Material variability is incorporated by assigning random variations in elastic properties within Voronoi cells. The computational model is shown to reproduce the primary aspects of the response observed in experiments, including the growth of a family of radial cracks from the point of impact.
Our goal is compression of massive-scale grid-structured data, such as the multi-terabyte output of a high-fidelity computational simulation. For such data sets, we have developed a new software package called TuckerMPI, a parallel C++/MPI software package for compressing distributed data. The approach is based on treating the data as a tensor, i.e., a multidimensional array, and computing its truncated Tucker decomposition, a higher-order analogue to the truncated singular value decomposition of a matrix. The result is a low-rank approximation of the original tensor-structured data. Compression efficiency is achieved by detecting latent global structure within the data, which we contrast to most compression methods that are focused on local structure. In this work, we describe TuckerMPI, our implementation of the truncated Tucker decomposition, including details of the data distribution and in-memory layouts, the parallel and serial implementations of the key kernels, and analysis of the storage, communication, and computational costs. We test the software on 4.5 and 6.7 terabyte data sets distributed across 100 s of nodes (1,000 s of MPI processes), achieving compression ratios between 100 and 200,000×, which equates to 99-99.999% compression (depending on the desired accuracy) in substantially less time than it would take to even read the same dataset from a parallel file system. Moreover, we show that our method also allows for reconstruction of partial or down-sampled data on a single node, without a parallel computer so long as the reconstructed portion is small enough to fit on a single machine, e.g., in the instance of reconstructing/visualizing a single down-sampled time step or computing summary statistics. The code is available at https://gitlab.com/tensors/TuckerMPI.
Arc flash hazard prediction methods have become more sophisticated because the knowledge about arc flash phenomenon has advanced since the publication of IEEE Std. 1584-2002 [17]. The IEEE Std. 1584-2018 [13] has added parameters for more accurate arc flash incident energy, arcing current and protection boundary estimation. The parameters in the updated estimation models include electrode configuration, open circuit voltage, bolted fault current, arc duration, gap width, working distance, and enclosure dimension. The sensitivity and effect changes of other parameters have been discussed the previous literatures [8] [9] [11] [2] [12] [15], this paper explains the fundamental theory on the selection of electrode configurations and performs sensitivity analysis of the enclosure dimension, that have been introduced in the IEEE Std. 1584-2018. According to the newly published model for incident energy (IE) estimation, the IE between VCB (Vertical Electrodes inside a metal Box) and HCB (Horizontal Electrodes inside a metal Box) can differ by a factor of two with other parameters constant. Using HCB as the worst-case scenario to determine the personal protection requirements [7] [10] may not be the best practice in all circumstances. This paper provides guidance for electrode configuration selection and a sensitivity analysis for determining a reasonable engineering margin when actual dimension is not available.
We illustrate a theoretical side-channel analysis on the intermediate rounds of AES, using only the Hamming weights of the bytes registered after the S-box operation. Input and output state values are unknown. Simulations and a blind test were used to show the feasibility of the analysis under ideal conditions. General applicability of the idea and possible extensions are discussed, as well as limiting assumptions. Some implementation approaches are described in Appendix A, in the case of constrained computing capabilities (desktop or laptop).
Ali, Amir; Kim, Hyun G.; Hattar, Khalid M.; Briggs, Samuel; Jun Park, Dong; Hwan Park, Jung; Lee, Youho
The concept of coating the currently used nuclear fuel cladding (zirconium-based alloy, typically Zircaloy-4 or Zirc-4) with an oxidation preventive layer is a progressing Accident tolerant Fuel (ATF) candidate alloys. The coated Zirc-4-based alloys could be a solution to suppress undesirable fast reaction kinetics with high-temperature steam. Zirc-4 has been the most preferred cladding material in pressurized water reactors (PWRs). Chromium (Cr) based alloys as a coating material provides excellent corrosion protection and good strength and wear resistance. This paper presents the surface wettability measurements and pool boiling Critical Heat Flux (CHF) for Cr-coated Zirc-4 claddings pre- and post-exposure to an ion irradiation environment. The wettability measurements, including static contact angle (contact angle, θ) and average surface roughness (surface roughness, Ra), are introduced for samples of different coating thicknesses (5–30 μm thick). The coatings fabricated by the cold spray of Cr-Al particles to 10 mm × 10 mm × 1.95 mm Zirc-4 substrates. Post fabrication, a Pilgering (cold rolling) process, was applied to finalize the coating thickness and resulted in a significant reduction in surface roughness of initially fabricated rough surfaces. The process produced three distinguished samples 5-μm unpolished (as machined), 5-μm, and 30-μm polished (cold rolled). The measurements are presented for the three surfaces and bare Zirc-4 as a baseline surface. The contact angle analyses were implemented in theoretical models from the literature to predict pool boiling CHF. Pool boiling experiments were conducted to measure the pool boiling CHF values and compare them to the predicted values. Scanning Electron Microscope (SEM) images and Energy Dispersive X-ray Spectroscopy (EDS) analysis was performed to characterize the surfaces for better understanding and interpreting the results. The SEM images showed localized surface damage due to ion irradiation. No recognized change in the measured surface roughness due to ion irradiation. The contact angles of irradiated Cr-coated surfaces are consistently higher (10°) than pre-irradiated surfaces. Decreasing the Cr-coating layer thickness resulted in lower contact angle pre- and post- ion irradiation. The predicted pool boiling CHF using the Kandlikar model is in good agreement with the experimentally measured CHF values within ±12% for all samples.
Subscale wind turbines can be installed in the field for the development of wind technologies, for which the blade aerodynamics can be designed in a way similar to that of a full-scale wind turbine. However, it is not clear whether the wake of a subscale turbine, which is located closer to the ground and faces different incoming turbulence, is also similar to that of a full-scale wind turbine. In this work we investigate the wakes from a full-scale wind turbine of rotor diameter 80 m and a subscale wind turbine of rotor diameter of 27 m using large-eddy simulation with the turbine blades and nacelle modeled using actuator surface models. The blade aerodynamics of the two turbines are the same. In the simulations, the two turbines also face the same turbulent boundary inflows. The computed results show differences between the two turbines for both velocity deficits and turbine-added turbulence kinetic energy. Such differences are further analyzed by examining the mean kinetic energy equation.
This paper presents a techno-economic analysis of behind-the-meter (BTM) solar photovoltaic (PV) and battery energy storage systems (BESS) applied to an Electric Vehicle (EV) fast-charging station. The goal is to estimate the maximum return on investment (ROI) that can be obtained for optimum BESS and PV size and their operation. Fast charging is a technology that will speed up mass adoption of EVs, which currently requires several hours to achieve full recharge in level 1 or 2 chargers. Fast chargers demand from tens to hundreds of kilowatts from the distribution grid, potentially leading to system congestion and overload. The problem is formulated as a linear program that obtains the size of PV, power and energy ratings of BESS as well as charging and discharging scheduling of the storage system to maximize ROI under operational constraints of BESS and PV. The revenue are cost-savings of demand and time-of-use charges, with a penalty for BESS degradation. We have considered Los Angeles Department of Water and Power tariff A-2 and fast charger data derived from the EV Project. The results show that a 46.5 kW/28.3 kWh BESS can obtain a ROI of about $22.4k over 10 years for a small 4-port fast-charging station.
The PSL has reviewed the documentation and data provided by NNSS–Livermore Operations with respect to this proficiency test. This proficiency test was performed to assess NNSS–Livermore Operations’ ability to perform scattering parameter calibrations. The level of documentation was satisfactory. On 5/19/2020, NNSS–Livermore Operations reported the data for the proficiency test conducted on the attenuator. NNSS–Livermore Operations performed this proficiency test using an Anritsu vector network analyzer, an electronic calibration module, and verification kit. The PSL used a Keysight vector network analyzer and mechanical calibration kit. The PSL results included in this proficiency test report were taken on June 23, 2020.
In this work we provide direct evidence of shock-induced melting and associated kinetics in a porous solid (aluminum powder) using time-resolved x-ray diffraction. Unambiguous evidence of melting in 50% porous aluminum (Al) powder samples, shocked to peak pressures between ∼13-19GPa, was provided by the broadening of the Debye-Scherrer ring corresponding to the (111) peak. Shocked Al powder did not melt completely in any of our experiments within the durations of measurement. Incomplete (partial) melting of the powder, even after several hundreds of nanoseconds of shock loading, provides insights into thermal transport with Al powder particles under high-pressure dynamic loading. Such insights are quite valuable for developing well-constrained melting models and thermodynamic equations of state for porous Al and other porous solids relevant to planetary and materials science.
Lim, Jinhyuk; Kim, Minseob; Duwal, Sakun; Ohishi, Yasuo; Hrubiak, Rostislav; Tse, John S.; Yoo, Choong-Shik
We have studied the compression behavior of H2-He mixtures in comparison with pure H2 and He using powder synchrotron x-ray diffraction and present the pressure-volume (PV) compression data of H2-He mixtures to 160 GPa. The results indicate that both H2 and He in H2-He mixtures remain in hcp to the maximum pressure studied, yet develop a substantial level of lattice distortion in the (100) plane, most profound in He-rich solids and below 66 GPa. The measured PV data also indicate softening of He (or H2)-rich lattice upon increasing the level of the guest H2 (or He) concentration. We suggest that the observed softening and lattice distortion are due to a substitutional incorporation of H2 (guest) molecules into the basal plane of hcp-He (host) lattice and, thereby, reflect the miscibility between H2 and He in H2-He mixtures. Interestingly, solid He exhibits a lesser degree of preferred orientation in H2-He mixtures than in pure He, likely due to the presence of solid H2 disturbing the crystalline ordering of He-rich solids. Finally, the present PV compression data of H2-rich and He-rich solids to 160 GPa deviate from those of pure H2 and pure He above ~70 and 45 GPa respectively, providing new constraints for development of the EOS for H2-He mixtures for planetary models.
The Port of Alaska in Anchorage enables the economic vitality of the Municipality of Anchorage and State of Alaska. It also provides significant support to defense activities across Alaska, especially to the Joint Base Elmendorf-Richardson (JBER) that is immediately adjacent to the Port. For this reason, stakeholders are interested in the resilience of the Ports operations. This report documents a preliminary feasibility analysis for developing an energy system that increases electric supply resilience for the Port and for a specific location inside JBER. The project concept emerged from prior work led by the Municipality of Anchorage and consultation with Port stakeholders. The project consists of a microgrid with PV, storage and diesel generation, capable of supplying electricity to loads at the Port a specific JBER location during utility outages, while also delivering economic value during blue-sky conditions. The study aims to estimate the size, configuration and concept of operations based on existing infrastructure and limited demand data. It also explores potential project benefits and challenges. The report goal is to inform further stakeholder consultation and next steps.
Richardson, Christopher J.K.; Lordi, Vincenzo; Misra, Shashank; Shabani, Javad
Quantum computing, sensing, and communications are emerging technologies that may circumvent known limitations of their existing traditional counterparts. While the promises of these technologies are currently narrow in scope, it is possible that they will broadly impact our lives by revolutionizing the capabilities of data centers and medical diagnostics, for example. At the heart of these technologies is the use of a quantum object to contain information, called a quantum bit or qubit. Current realizations of qubits exist in a broad variety of material systems, including individual spins in semiconductors or insulators, superconducting circuits, and trapped ions. Further advancement of qubits requires significant contributions from materials science in areas of materials selection, synthesis, fabrication, simulation and characterization. Here, we discuss some of the needs and opportunities for contributions to advance the fundamental understanding of materials used in quantum information applications.
The MACCS (MELCOR Accident Consequence Code System) code is the U.S. Nuclear Regulatory Commission (NRC) tool used to perform probabilistic health and economic consequence assessments for atmospheric releases of radionuclides. It is also used by international organizations, both reactor owners and regulators. It is intended and most commonly used for hypothetical accidents that could potentially occur in the future rather than to evaluate past accidents or to provide emergency response during an ongoing accident. It is designed to support probabilistic risk and consequence analyses and is used by the NRC, U.S. nuclear licensees, the Department of Energy, and international vendors, licensees, and regulators. This report describes the modeling framework, implementation, verification, and benchmarking of a GDP-based model for economic losses that has recently been developed as an alternative to the original cost-based economic loss model in MACCS. The GDP-based model has its roots in a code developed by Sandia National Laboratories for the Department of Homeland Security to estimate short-term losses from natural and manmade accidents, called the Regional Economic Accounting analysis tool (REAcct). This model was adapted and modified for MACCS and is now called the Regional Disruption Economic Impact Model (RDEIM). It is based on input-output theory, which is widely used in economic modeling. It accounts for direct losses to a disrupted region affected by an accident, indirect losses to the national economy due to disruption of the supply chain, and induced losses from reduced spending by displaced workers. RDEIM differs from REAcct in its treatment and estimation of indirect loss multipliers, elimination of double counting associated with inter-industry trade in the affected area, and that it is designed to be used to estimate impacts for extended periods that can occur from a major nuclear reactor accident, such as the one that occurred at the Fukushima Daiichi site in Japan. Most input-output models do not account for economic adaptation and recovery, and in this regard RDEIM differs from its parent, REAcct, because it allows for a user-definable national recovery period. Implementation of a recovery period was one of several recommendations made by an independent peer review panel to ensure that RDEIM is state-of-practice. For this and several other reasons, RDEIM differs from REAcct. Both the original and the RDEIM economic loss models account for costs from evacuation and relocation, decontamination, depreciation, and condemnation. Where the original model accounts for an expected rate of return, based on the value of property, that is lost during interdiction, the RDEIM model instead accounts for losses of GDP based on the industrial sectors located within a county. The original model includes costs for disposal of crops and milk that the RDEIM model currently does not, but these costs tend to contribute insignificantly to the overall losses. This document discusses three verification exercises to demonstrate that the RDEIM model is implemented correctly in MACCS. It also describes a benchmark study at five nuclear power plants chosen to represent the spectrum of U.S. commercial sites. The benchmarks provide perspective on the expected differences between the RDEIM and the original cost-based economic loss models. The RDEIM model is shown to consistently predict larger losses than the original model, probably in part because it accounts for national losses by including indirect and induced losses; whereas, the original model only accounts for regional losses. Nonetheless, the RDEIM model predicts losses that are remarkably consistent with the original cost-based model, differing by 16% at most for the five sites combined with three source terms considered in this benchmark.
Boronic acid-modified polymers (BAMPs) can interact with glycoproteins and other glycosylated compounds through covalent binding of the boronic acid moieties to saccharide residues. As a first step toward evaluating the utility of BAMPs as SARS-CoV-2 antiviral agents, this COVID-19 rapid response LDRD was intended to examine the effect of BAMPs on SARS-CoV-2 spike glycoprotein and its subsequent binding with ACE2 receptor protein. Multiple different approaches were attempted in order to determine whether BAMPs based on poly(ethylene glycol) and poly(ethylenimine) bind the spike protein, but failed to produce a definitive answer. However, two different enzyme-linked immunosorbent assays clearly showed no discernable effect of boronic acid in inhibiting spike-ACE2 binding.
A novel derivative of a previously-published polymeric material has been synthesized and developed into an easily-sprayable coating. Surface characterization of coatings confirm correct elemental presence, and viral assays reveal quantitative elimination of MS2 bacteriophage and Phi6 bacteriophage, surrogates used for SARS-CoV-2, in as little as 5 minutes upon contact. Furthermore, an N95 mask was dip-coated in the polymer solution and analyzed through microscopy and filtration efficacy testing. Though coating was successful, electrostatic interactions between mask layers and polymer reduced filtration efficacy significantly. As such, we expect the current results of this work to be applicable on non-respiratory PPE and on solid substrates of commonly-touched surfaces for rapid self-decontamination.
The Strategic Performance Evaluation Measurement Plan (PEMP) Scorecard, now housed in QuickScore, is an assurance and governance data scorecard which shows the health of the labs against the current fiscal year PEMP objectives. POC's for PEMP objective owners are requested to provide status of their PEMP objective, capture the top 6 cumulative accomplishments, top 6 issues, which include IFR issues and provide mitigation and or improvement action plans. Updated scorecards are used in OMR and BOM GS&S committee
In March and April of 2020 there was widespread concern about availability of medical resources required to treat Covid-19 patients who become seriously ill. A simulation model of supply management was developed to aid understanding of how to best manage available supplies and channel new production. Forecasted demands for critical therapeutic resources have tremendous uncertainty, largely due to uncertainties about the number and timing of patient arrivals. It is therefore essential to evaluate any process for managing supplies in view of this uncertainty. To support such evaluations, we developed a modeling framework that would allow an integrated assessment in the context of uncertainty quantification. At the time of writing there has been no need to execute this framework because adaptations of the medical system have been able to respond effectively to the outbreak. This report documents the framework and its implemented components should need later arise for its application.
We present a numerical framework for recovering unknown non-autonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.
This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.
The Sandia National Laboratories Physical Security Center of Excellence (PSCOE) has been tasked by the Department of State (DOS) Bureau of Diplomatic Security Research and Development branch to investigate the potential anti-climb benefits of newly developed skid-resistant paint coatings - one light base and one dark base. DOS is interested in studying the application of the coatings on passive barriers commonly used at diplomatic facilities. The purpose of the anti-climb coating in this context is to deter and delay adversaries from climbing onto the passive barriers. PSCOE was tasked to perform delay testing that focused on the effectiveness of the coatings on the two DOS perimeter passive barriers - the DS-41 anti-ram fence and a 9 foot high by 1 foot thick reinforced concrete wall intended to mimic the DS-30 anti-ram perimeter wall. PSCOE was also tasked in performing skid-resistance testing using a British Pendulum skid-resistance tester. Delay testing and skid-resistance testing were also performed on two different passive barriers without any anti-climb coating to determine a baseline.
The State-of-the-Art Reactor Consequence Analyses (SOARCA) project has focused on best estimate analyses and uncertainty analysis for postulated accidents at specific nuclear power plants. The consequences of these accidents are estimated using the simulation tools MELCOR and MACCS. To understand which uncertain input variables are important to determining these consequences, analysts have performed sensitivity analyses. The tool used to perform these sensitivity analyses in previous SOARCA work, CompModSA, is no longer supported. Therefore, the current work focuses on migrating these analyses to another tool and evaluating its performance. Dakota, which is a tool developed at Sandia National Laboratories, is used in this work. Sensitivity results are created for three analyses from the SOARCA Surry UA. Though CompModSA and Dakota vary slightly in their algorithms and implementation, their sensitivity results generally agree, which gives confidence in the Dakota approach and increases confidence in the original analyses. It is likely that this methodology is extendable to the rest of SOARCA analyses.
Attaway is a recently installed High-Performance Computing (HPC) machine at Sandia National Labs that is 70% water-cooled and 30% air-cooled. This machine, supplied by Penguin Computing, uses a novel new cooling system from Chilldyne that operates in a vacuum, preventing water leaks. If water-cooling is to fail, fans inside of each node will ramp up to do 100% of the cooling on Attaway. Various tests were completed on Attaway to determine the robustness of its cooling system as well as its ability to respond to sudden changes in states. These changes include an immediate change from an idle compute load to full load (Linpack) as well as running Linpack without any water cooling from Attaway's CDUs. It was discovered that Attaway could respond to sudden compute load changes very well, never throttling any nodes. When Linpack was run without water cooling, the system was able to operate for a short time before throttling happened.
Compact diffusion bonded heat exchangers are essential for high pressure heat exchange, but they are subject to thermal fatigue and ramp rate limitations. Simulation of these geometries is challenging with a large range of length and time scales from thousands of mm-sized microchannels inside a m-sized heat exchanger. Multi-physics simulations including thermal, fluid, and solid mechanics components are being used to predict stress within the heat exchangers under these conditions. These predictions can then be used to understand thermal ramp rate limitations while keeping maximum stresses low as well as fatigue life predictions from well-known empirical models.
Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.
The U.S. Nuclear Regulatory Commission (NRC) performed a first-of-a-kind uncertainty analysis (UA) of the accident progression, radiological releases, and offsite consequences for the State-of- the-Art Reactor Consequence Analyses (SOARCA) of an unmitigated long-term station blackout (LTSBO) severe accident scenario at the Peach Bottom Atomic Power Station. The objective of the UA was to evaluate the robustness of the SOARCA deterministic "best estimate results and conclusions documented in NUREG-1935, and to develop insight into the overall sensitivity of the SOARCA results to uncertainty in key modeling inputs. The study was completed in 2015 and documented in NUREG/CR-7155. Since 2015, two other SOARCA UAs were completed for two pressurized water reactor (PWR) plants. The PWR UAs incrementally updated the approach and methodology, including using the latest release of the MELCOR 2.2 computer code. There were also advances made in the state-of-the-art modeling related to NRC efforts using the Peach Bottom model in NUREG-2206, which provided the technical basis for the containment protection and release reduction rulemaking for boiling water reactors with Mark I and Mark II containments. This report documents the input model changes from the NUREG/CR-7155 study and performs a small number of reference calculations to assess the changes of the new computer code and the model input updates. The objective of the work is to verify whether the updated Peach Bottom MELCOR model and updated version of MELCOR support the conclusions formed in the Peach Bottom SOARCA UA by performing these representative calculations.
This paper describes the source term calculation documented in SAND2011-0128, ''Accident Source Terms for Light-Water Nuclear Power Plants Using High-Burnup or MOX Fuel'' and describes one method for implementing the calculation in MATLAB.
The flow and cavitation behavior inside fuel injectors is known to affect spray development, mixing and combustion characteristics. While diesel fuel injectors with converging and hydro-eroded holes are generally known to limit cavitation and feature higher discharge coefficients during the steady period of injection, less is known about the flow during transient periods corresponding to needle opening and closing. Multiple injection strategies involve short injections, multiplying these aspects and giving them a growing importance as part of the fuel delivery process. In this study, single-hole transparent nozzles were manufactured with the same hole inlet radius and diameter as the Engine Combustion Network Spray D nozzle, mounted to a modified version of a common-rail Spray A injector body and needle. Needle opening and closing periods were visualized with stereoscopic high-speed microscopy at injection pressures relevant to modern diesel engines. Time-resolved sac pressure was extracted via elastic deformation analysis of the transparent nozzles. Sources of cavitation were observed and tracked, enabling the identification of a gas exchange process after the end of injection with ingestion of chamber gas into the sac and orifice. We observed that the gas exchange contributed widely to disrupting the start of injection and outlet flow during the subsequent injection event.
Brittan, Andrew M.; Mahaffey, Jacob T.; Colgan, Nathan E.; Elbakhshwan, Mohamed; Anderson, Mark H.
This study investigates the effectiveness of Cu as a corrosion barrier in supercritical carbon dioxide (s−CO2) by coating 316 stainless steel (316) with various thicknesses of Cu. 316 exposed to s−CO2 with 50 ppm CO showed a reduction in oxidation corresponding to the thickness of its Cu barrier coating. Additionally, a continuous Cu layer between the environment and the alloy was found to correlate to an elimination of carburization and corrosion-related mechanical degradation. This Cu coating technique could be applied over a variety of temperatures to improve the corrosion resistance of alloys that are susceptible to carburization.
Fuel injection rate laws are one of the most important pieces of information needed when modeling engine combustion with computational fluid dynamics. In this study, a simple phenomenological model of a common-rail injector was developed and calibrated for the Bosch CRI2.2 platform. The model requires three tunable parameter fits, making it relatively easy to calibrate and suitable for injector modeling when high-fidelity information about the internal injector’s geometry and electrical circuit details are not available. Each injection pulse is modeled as a sequence of up to four stages: an injection needle mechanical opening transient, a full-lift viscous flow inertial transient, a Bernoulli steady-state stage, and a needle descent transient. Parameters for each stage are obtained as polynomial fits from measured injection rate properties. The model enforces total injected mass, and the intermediate stages are only introduced if the injection pulse duration is long enough. Experimental rates of injection from two separate campaigns on the same injector were used to calibrate the model. The model was first validated against measured injection rate laws featuring pilot injections, short partially premixed combustion pulses, and conventional diesel combustion injection strategies. Then, it was employed as an input to engine computational fluid dynamics simulations, which were run to simulate experiments of mixture formation in an optically accessible light-duty diesel engine. It was found that, though simple, this model is capable of predicting both pilot and main injection pulse mass flow rates well: the simulations yielded accurate predictions of in-cylinder equivalence ratio distributions from injection strategies for both partially premixed combustion and pilot injections. Also, once calibrated, the model produced appropriate results for a wide range of injected mass and rail pressure values. Finally, it was observed that usage of such a relatively simple model can be a good choice when high-fidelity injection rate input and highly detailed information of the injector’s geometry and operation are not available, particularly as noticeable discrepancies can be present also among different experimental campaigns on similar hardware.
Faraggiana, E.; Whitlam, C.; Chapman, J.; Hillis, A.; Roesner, J.; Hann, M.; Greaves, D.; Yu, Y.H.; Ruehl, Kelley M.; Masters, I.; Foster, G.; Stockman, G.
A submerged wave device generates energy from the relative motion of floating bodies. In WaveSub, three floats are joined to a reactor; each connected to a spring and generator. Electricity generated damps the orbital movements of the floats. The forces are non-linear and each float interacts with the others. Tuning to the wave climate is achieved by changing the line lengths, so there is a need to understand the performance trade-offs for a large number of configurations. This requires an efficient, large displacement, multidirectional, multi-body numerical scheme. Results from a 1/25 scale wave basin experiment are described. Here, we show that a time domain linear potential flow formulation (Nemoh, WEC-Sim) can match the tank testing provided that suitably tuned drag coefficients are employed. Inviscid linear potential models can match some wave device experiments; however, additional viscous terms generally provide better accuracy. Scale experiments are also prone to mechanical friction, and we estimate friction terms to improve the correlation further. The resulting error in mean power between numerical and physical models is approximately 10%. Predicted device movement shows a good match. Overall, drag terms in time domain wave energy modelling will improve simulation accuracy in wave renewable energy device design.
Communication hypergraph model was proposed in a two-phase setting for encapsulating multiple communication cost metrics (bandwidth and latency), which are proven to be important in parallelizing irregular applications. In the first phase, computational-task-to-processor assignment is performed with the objective of minimizing total volume while maintaining computational load balance. In the second phase, communication-task-to-processor assignment is performed with the objective of minimizing total number of messages while maintaining communication-volume balance. The reduce-communication hypergraph model suffers from failing to correctly encapsulate send-volume balancing. We propose a novel vertex weighting scheme that enables part weights to correctly encode send-volume loads of processors for send-volume balancing. The model also suffers from increasing the total communication volume during partitioning. To decrease this increase, we propose a method that utilizes the recursive bipartitioning framework and refines each bipartition by vertex swaps. For performance evaluation, we consider column-parallel SpMV, which is one of the most widely known applications in which the reduce-task assignment problem arises. Extensive experiments on 313 matrices show that, compared to the existing model, the proposed models achieve considerable improvements in all communication cost metrics. These improvements lead to an average decrease of 30 percent in parallel SpMV time on 512 processors for 70 matrices with high irregularity.
We present a novel formulation for startup cost computation in the unit commitment problem (UC). Both our proposed formulation and existing formulations in the literature are placed in a formal, theoretical dominance hierarchy based on their respective linear programming relaxations. Our proposed formulation is tested empirically against existing formulations on large-scale UC instances drawn from real-world data. While requiring more variables than the current state-of-the-art formulation, our proposed formulation requires fewer constraints, and is empirically demonstrated to be as tight as a perfect formulation for startup costs. This tightening can reduce the computational burden in comparison to existing formulations, especially for UC instances with large reserve margins and high penetration levels of renewables.
Impurity seeding studies in the small angle slot (SAS) divertor at DIII-D have revealed a strong relationship between the detachment onset and pedestal characteristics with both target geometry and impurity species. N2 seeding in the slot has led to the first simultaneous observation of detachment on the entire suite of boundary diagnostics viewing the SAS without degradation of core confinement. SOLPS-ITER simulations with D+C+N, full cross field drifts, and n-n collisions activated are performed for the first time in DIII-D to interpret the behavior. This highlights a strong effect of divertor configuration and plasma drifts on the recycling source distribution with significant consequences on plasma flows. Flow reversal is found for both main ions and impurities affecting strongly the impurity transport and providing an explanation for the observed dependence on the strike point location of the detachment onset and impurity leakage found in the experiments. Matched discharges with either nitrogen or neon injection show that while nitrogen does not significantly affect the pedestal, neon leads to increased pedestal pressure gradients and improved pedestal stability. Little nitrogen penetrates in the core, but a significant amount of neon is found in the pedestal consistent with the different ionization potentials of the two impurities. This work demonstrates that neutral and impurity distributions in the divertor can be controlled through variations in strike point locations in a fixed baffle structure. Divertor geometry combined with impurity seeding enables mitigated divertor heat flux balancing core contamination, thus leading to enhanced divertor dissipation and improved core-edge compatibility, which are essential for ITER and for future fusion reactors.
Magnetized Liner Inertial Fusion (MagLIF) at Sandia National Laboratories involves a laser preheating stage where a few-ns laser pulse passes through a few-micron-thick plastic window to preheat gaseous fusion fuel contained within the MagLIF target. Interactions with this window reduce heating efficiency and mix window and target materials into the fuel. A recently proposed idea called "Laser Gate"involves removing the window well before the preheating laser is applied. In this article, we present experimental proof-of-principle results for a pulsed-power implementation of Laser Gate, where a thin current-carrying wire weakens the perimeter of the window, allowing the fuel pressure to push the window open and away from the preheating laser path. For this effort, transparent targets were fabricated and a test facility capable of studying this version of Laser Gate was developed. A 12-frame bright-field laser schlieren/shadowgraphy imaging system captured the window opening dynamics on microsecond timescales. The images reveal that the window remains largely intact as it opens and detaches from the target. A column of escaping pressurized gas appears to prevent the detached window from inadvertently moving into the preheating laser path.
Deliverable Description: Identify and evaluate options for fuel and basket modifications, for dual-purpose canisters (DPCs) to be loaded in the future, that would substantially reduce the probability of post closure criticality after waste package breach and flooding with ground water. Planned work in FY20 will examine the feasibility of criticality control features, particularly neutron absorbing inserts or replacement channels for boiling water reactor (BWR) fuel assemblies. The expected outcome is additional engineering information that can be used to guide the R&D program, and to support future stakeholder interactions. This document will be incorporated into planned deliverable DPC Disposal Concepts of Operation (M3 SF-20SNO10305052, 18S ep20).
The Dial-A-Cluster (DAC) model allows interactive visualization of multivariate time series data. A multivariate time series dataset consists of an ensemble of data points, where each data point consists of a set of time series curves. The example of a DAC dataset used in this guide is a collection of 100 cities in the United States, where each city collects a year's worth of weather data, including daily temperature, humidity, and wind speed measurements.
Chemical kinetics simulations are used to explore whether detailed measurements of relevant chemical species during the oxidation of very dilute fuels (less than 1 Torr partial pressure) in a high-pressure plug flow reactor (PFR) can predict autoignition propensity. We find that for many fuels the timescale for the onset of spontaneous oxidation in dilute fuel/air mixtures in a simple PFR is similar to the 1st-stage ignition delay time (IDT) at stoichiometric engine-relevant conditions. For those fuels that deviate from this simple trend, the deviation is closely related to the peak rate of production of OH, HO2, CH2O, and CO2 formed during oxidation. We use these insights to show that an accurate correlation between simulated profiles of these species in a PFR and 1st-stage IDT can be developed using convolutional neural networks. Our simulations suggest that the accuracy of such a correlation is 10–50%, which is appropriate for rapid fuel screening and may be sufficient for predictive fuel performance modeling.
The US Department of Energy (DOE) Nuclear Energy Research Initiative funded the design and construction of the Seven Percent Critical Experiment (7uPCX) at Sandia National Laboratories. The start-up of the experiment facility and the execution of the experiments described here were funded by the DOE Nuclear Criticality Safety Program. The 7uPCX is designed to investigate critical systems with fuel for light water reactors in the enrichment range above 5 % 235U. The 7uPCX assembly is a water-moderated and -reflected array of aluminum-clad square-pitched U(6.90 %)02 fuel rods. Other critical experiments performed in the 7uPCX assembly are documented in LEU-COMP-THERM-078, LEU-COMP-THERM-080, LEU-COMP-THERM-096, and LEU-COMP-THERM-097.
Subsidence monitoring is a crucial component to understanding cavern integrity of salt storage caverns. This report looks at the historical and current subsidence monitoring program and includes interpretation of the data from the Bayou Choctaw Strategic Petroleum Reserve site. The current monitoring program consists of an annual elevation survey as well as GPS and tiltmeter instruments above both Cavern 4 and Cavern 20. This year's level and rod survey indicates little subsidence across the site. In addition, the GPS and tiltmeter instruments do not indicate any substantial movement above caverns 4 and 20. As such, there is no reason to indicate any of the caverns at Bayou Choctaw have lost integrity.
In this paper, we continue our efforts to exploit optimization and control ideas as a common foundation for the development of property-preserving numerical methods. Here we focus on a class of scalar advection equations whose solutions have fixed mass in a given Eulerian region and constant bounds in any Lagrangian volume. Our approach separates discretization of the equations from the preservation of their solution properties by treating the latter as optimization constraints. This relieves the discretization process from having to comply with additional restrictions and makes stability and accuracy the sole considerations in its design. A property-preserving solution is then sought as a state that minimizes the distance to an optimally accurate but not property-preserving target solution computed by the scheme, subject to constraints enforcing discrete proxies of the desired properties. Furthermore, we consider two such formulations in which the optimization variables are given by the nodal solution values and suitably defined nodal fluxes, respectively. A key result of the paper reveals that a standard Algebraic Flux Correction (AFC) scheme is a modified version of the second formulation obtained by shrinking its feasible set to a hypercube. In conclusion, we present numerical studies illustrating the optimization-based formulations and comparing them with AFC
Li-ion batteries currently dominate electrochemical energy storage for grid-scale applications, but there are promising aqueous battery technologies on the path to commercial adoption. Though aqueous batteries are considered lower risk, they can still undergo problematic degradation processes. This perspective details the degradation that aqueous batteries can experience during normal and abusive operation, and how these processes can even lead to cascading failure. We outline methods for studying these phenomena at the material and single-cell level. Considering reliability and safety studies early in technology development will facilitate translation of emerging aqueous batteries from the lab to the field.
A physically unclonable function (PUF) is an embedded hardware security measure that provides protection against counterfeiting. In this article, we present our work on using an array of randomly magnetized micrometer-sized ferromagnetic bars (micromagnets) as a PUF. We employ a 4μm thick surface layer of nitrogen-vacancy (NV) centers in diamond to image the magnetic field from each micromagnet in the array, after which we extract the magnetic polarity of each micromagnet using image analysis techniques. Finally, after evaluating the randomness of the micromagnet array PUF and the sensitivity of the NV readout, we conclude by discussing the possible future enhancements for improved security and magnetic readout.
Sierra/SolidMechanics (Sierra/SM) is a Lagrangian, three-dimensional code for finite element analysis of solids and structures. It provides capabilities for explicit dynamic, implicit quasistatic and dynamic analyses. The explicit dynamics capabilities allow for the efficient and robust solution of models with extensive contact subjected to large, suddenly applied loads. For implicit problems, Sierra/SM uses a multi-level iterative solver, which enables it to effectively solve problems with large deformations, nonlinear material behavior, and contact. Sierra/SM has a versatile library of continuum and structural elements, and a large library of material models. The code is written for parallel computing environments enabling scalable solutions of extremely large problems for both implicit and explicit analyses. It is built on the SIERRA Framework, which facilitates coupling with other SIERRA mechanics codes . This document describes the functionality and input syntax for Sierra/SM.
Rempe, Susan; Lubkowski, Jacek; Vanegas, Juan; Chan, Wai K.; Lorenzi, Philip L.; Weinstein, John N.; Sukharev, Sergei; Fushman, David; Anishkin, Andriy; Wlodawer, Alexander
Two bacterial type II l-asparaginases, from Escherichia coli and Dickeya chrysanthemi, have played a critical role for more than 40 years as therapeutic agents against juvenile leukemias and lymphomas. Despite a long history of successful pharmacological applications and the apparent simplicity of the catalytic reaction, controversies still exist regarding major steps of the mechanism. In this report, we provide a detailed description of the reaction catalyzed by E. coli type II l-asparaginase (EcAII). Our model was developed on the basis of new structural and biochemical experiments combined with previously published data. The proposed mechanism is supported by quantum chemistry calculations based on density functional theory. We provide strong evidence that EcAII catalyzes the reaction according to the double-displacement (ping-pong) mechanism, with formation of a covalent intermediate. Several steps of catalysis by EcAII are unique when compared to reactions catalyzed by other known hydrolytic enzymes. Here, the reaction is initiated by a weak nucleophile, threonine, without direct assistance of a general base, although a distant general base is identified. Furthermore, tetrahedral intermediates formed during the catalytic process are stabilized by a never previously described motif. Although the scheme of the catalytic mechanism was developed only on the basis of data obtained from EcAII and its variants, this novel mechanism of enzymatic hydrolysis could potentially apply to most (and possibly all) l-asparaginases.
We extend and improve recent results given by Singh and Watson on using classical bounds on the union of sets in a chance-constrained optimization problem. Specifically, we revisit the so-called Dawson and Sankoff bound that provided one of the best approximations of a chance constraint in the previous analysis. Next, we show that our work is a generalization of the previous work, and in fact the inequality employed previously is a very relaxed approximation with assumptions that do not generally hold. Computational results demonstrate on average over a 43% improvement in the bounds. As a byproduct, we provide an exact reformulation of the floor function in optimization models.
Maes, Noud; Skeen, Scott A.; Bardi, Michele; Fitzgerald, Russell P.; Malbec, Louis M.; Bruneaux, Gilles; Pickett, Lyle M.; Yasutomi, Koji; Martin, Glen
In a collaborative effort to identify key aspects of heavy-duty diesel injector behavior, the Engine Combustion Network (ECN) Spray C and Spray D injectors were characterized in three independent research laboratories using constant volume pre-burn vessels and a heated constant-pressure vessel. This work reports on experiments with nominally identical injectors used in different optically accessible combustion chambers, where one of the injectors was designed intentionally to promote cavitation. Optical diagnostic techniques specifically targeted liquid- and vapor-phase penetration, combustion indicators, and sooting behavior over a large range of ambient temperatures—from 850 K to 1100 K. Because the large-orifice injectors employed in this work result in flame lengths that extend well beyond the optical diagnostics’ field-of-view, a novel method using a characteristic volume is proposed for quantitative comparison of soot under such conditions. Further, the viability of extrapolating these measurements downstream is considered. The results reported in this publication explain trends and unique characteristics of the two different injectors over a range of conditions and serve as calibration targets for numerical efforts within the ECN consortium and beyond. Building on agreement for experimental results from different institutions under inert conditions, apparent differences found in combustion indicators and sooting behavior are addressed and explained. Ignition delay and soot onset are correlated and the results demonstrate the sensitivity of soot formation to the major species of the ambient gas (i.e., carbon dioxide, water, and nitrogen in the pre-burn ambient versus nitrogen only in the constant pressure vessel) when holding ambient oxygen volume percent constant.
We present the draft genome sequences of three Burkholderia thailandensis strains, E421, E426, and DW503. E421 consists of 90 contigs of 6,639,935 bp and 67.73% GC content. E426 consists of 106 contigs of 6,587,853 bp and 67.73% GC content. DW503 consists of 102 contigs of 6,458,767 bp and 67.64% GC content.
Density Functional Theory (DFT) calculations of electrode material properties in high energy density storage devices like lithium batteries have been standard practice for decades. In contrast, DFT modelling of explicit interfaces in batteries arguably lacks universally adopted methodology and needs further conceptual development. In this paper, we focus on solid-solid interfaces, which are ubiquitous not just in all-solid state batteries; liquid-electrolyte-based batteries often rely on thin, solid passivating films on electrode surfaces to function. We use metal anode calculations to illustrate that explicit interface models are critical for elucidating contact potentials, electric fields at interfaces, and kinetic stability with respect to parasitic reactions. The examples emphasize three key challenges: (1) the "dirty" nature of most battery electrode surfaces; (2) voltage calibration and control; and (3) the fact that interfacial structures are governed by kinetics, not thermodynamics. To meet these challenges, developing new computational techniques and importing insights from other electrochemical disciplines will be beneficial.
A novel metal-organic framework (MOF), Mn-DOBDC, has been synthesized in an effort to investigate the role of both the metal center and presence of free linker hydroxyls on the luminescent properties of DOBDC (2,5-dihydroxyterephthalic acid) containing MOFs. Co-MOF-74, RE-DOBDC (RE-Eu and Tb), and Mn-DOBDC have been synthesized and analyzed by powder X-ray diffraction (PXRD) and the fluorescent properties probed by UV-Vis spectroscopy and density functional theory (DFT). Mn-DOBDC has been synthesized by a new method involving a concurrent facile reflux synthesis and slow crystallization, resulting in yellow single crystals in monoclinic space group C2/c. Mn-DOBDC was further analyzed by single-crystal X-ray diffraction (SCXRD), scanning electron microscopy-energy-dispersive spectroscopy (SEM-EDS), and photoluminescent emission. Results indicate that the luminescent properties of the DOBDC linker are transferred to the three-dimensional structures of both the RE-DOBDC and Mn-DOBDC, which contain free hydroxyls on the linker. In Co-MOF-74 however, luminescence is quenched in the solid state due to binding of the phenolic hydroxyls within the MOF structure. Mn-DOBDC exhibits a ligand-based tunable emission that can be controlled in solution by the use of different solvents.
Accurate models of thermal runaway in lithium-ion batteries require quantitative knowledge of heat release during thermochemical processes. A capability to predict at least some aspects of heat release for a wide variety of candidate materials a priori is desirable. This work establishes a framework for predicting staged heat release from basic thermodynamic properties for layered metal-oxide cathodes. Available enthalpies relevant to thermal decomposition of layered metal-oxide cathodes are reviewed and assembled in this work to predict potential heat release in the presence of alkyl-carbonate electrolytes with varying state of charge. Cathode delithiation leads to a less stable metal oxide subject to phase transformations including oxygen release when heated. We recommend reaction enthalpies and show the thermal consequences of metal-oxide phase changes and solvent oxidation within the battery are of comparable magnitudes. Heats of reaction are related in this work to typical observations reported in the literature for species characterization and calorimetry. The methods and assembled databases of formation and reaction enthalpies in this work lay groundwork a new generation of thermal runaway models based on fundamental material thermodynamics, capable of predicting accurate maximum cell temperatures and hence cascading cell-to-cell propagation rates.